Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.
Alerts are supposed to start an investigation. Too often, they start translation: what is the system doing right now? That translation slows containment, splinters context, and stretches customer impact.
Asynchronous pipelines sit at the core of most modern systems. Message brokers accept traffic, consumers process it in the background, and downstream services depend on the results. When these systems fail, the failure rarely shows up where it starts.
Originally published to the Slight Reliability Podcast.
Causely’s expanded Datadog integration turns Datadog APM signals into system-level causal intelligence, helping teams understand how issues propagate across services and pinpoint true root cause.
How Causely uses FluxCD and GitOps to ship weekly on Kubernetes, keep clusters in sync, and wire up OpenTelemetry and Causely in a hands-on lab you can copy.
Gartner recognized Causely for maintaining a live causality graph and using continuous inference to identify the underlying driver behind changes in golden signals as they emerge, even when failures cascade across multiple services.
In a 50 to 100+ microservice environment with dense service-to-service dependencies, even small regressions can cascade silently. And slowing down isn’t an option. Leadership needs faster delivery and fewer incidents. This is why we built Reliability Delta.
Originally published as a livestream to e-After Work.
Originally posted to Intellyx by Jason English.
Originally posted as a livestream from OllyGarden.
Originally posted to TFIR by Monika Chauhan. Causely’s Severin Neumann explains how causal reasoning, MCP, and AI-driven automation are transforming SRE workflows and Kubernetes reliability.